Deviation correction detection method, system, equipment and medium
By acquiring material location data and target weld parameters, and using a welding prediction model for correction detection, the problem of needing to stop and adjust for unqualified welding in existing technologies has been solved, achieving a highly efficient and precise welding process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-18
- Publication Date
- 2026-04-14
Smart Images
Figure CN121855436A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of production equipment technology, and more specifically, to a method, system, equipment, and medium for corrective detection. Background Technology
[0002] The existing correction and inspection equipment detects the welding accuracy of materials in real time. If the detection result is that the welding is unqualified, the welding machine must be stopped, and the position of the welding material must be adjusted manually based on experience before the welding machine is restarted to weld the material and detect the welding position. This results in low production efficiency and inaccurate welding accuracy. Summary of the Invention
[0003] In view of this, the purpose of the present invention is to provide a method, system, device and medium for corrective detection, so as to improve production efficiency and welding accuracy at the same time.
[0004] Firstly, this application provides a method for corrective detection, comprising: Obtain the position data of the first material to be welded and the position data of the second material, as well as the target weld parameters corresponding to the first material and the second material; Based on the position data of the first material and the position data of the second material, a welding prediction model is used to predict the predicted weld parameters corresponding to the first material and the second material; based on the predicted weld parameters and the target weld parameters, the first moving distance corresponding to the first material and / or the second material is determined; based on the first moving distance, the correction mechanism is controlled to perform correction actions. Upon receiving the actual weld parameters corresponding to the first material and the second material, a second moving distance corresponding to the first material and / or the second material is determined based on the actual weld parameters and the target weld parameters; based on the second moving distance, the correction mechanism is controlled to perform correction actions.
[0005] Optionally, based on the predicted weld parameters and the target weld parameters, determining the first moving distance corresponding to the first material and / or the second material includes: Based on the predicted weld parameters, a vector operation algorithm is used to determine the first vector corresponding to the predicted weld parameters. Based on the target weld parameters, a vector operation algorithm is used to determine the second vector corresponding to the target weld parameters; Based on the first vector and the second vector, determine the first movement distance corresponding to the first material and / or the second material.
[0006] Optionally, determining the first movement distance corresponding to the first material and / or the second material based on the first vector and the second vector includes: Based on the first vector and the second vector, determine the direction of movement corresponding to the first moving distance; Based on the direction of movement, the first moving distance is determined to adjust the first material and / or the second material accordingly.
[0007] Optionally, based on the first travel distance, the correction mechanism is controlled to perform a correction action, including: Based on the position data of the first material and the first moving distance, the first offset distance is determined; The second offset distance is determined based on the position data of the second material and the first moving distance; Based on the first offset distance and the second offset distance, the movement of the correction mechanism is controlled to adjust the position of the first material and / or the second material.
[0008] Optionally, based on the first offset distance and the second offset distance, the action of the correction mechanism is controlled to adjust the position of the first material and / or the second material, including: When the first offset distance is greater than or equal to the second offset distance, the correction mechanism is controlled to adjust the position of the first material so that the first offset distance and the second offset distance are equal. When the first offset distance is less than the second offset distance and equal to it, the correction mechanism is controlled to adjust the position of the second material so that the first offset distance and the second offset distance are equal. When the first offset distance and the second offset distance are equal, the control mechanism is activated to adjust the positions of the first material and the second material so that the first offset distance and the second offset distance are zero.
[0009] Optionally, the deviation correction detection method provided in this application further includes: Obtain a training dataset; wherein the training dataset includes multiple training sample data; each training sample data includes the position data of the first material to be welded and the position data of the second material, as well as the target weld parameters; Based on the training dataset, iterative training is performed on the initial welding prediction model until the termination condition for iterative training is met. Then, based on the weights and thresholds of the initial welding prediction model updated during the last iteration, a new welding prediction model is obtained. The iterative training operation includes: Select target training sample data from the training dataset; The position data of the first material and the position data of the second material in the target training sample data are input into the initial welding prediction model. The initial welding prediction model receives the position data of the first material and the position data of the second material through the input layer, processes the position data of the second material and the position data of the second material through the hidden layer to obtain the predicted weld parameters, and then outputs the predicted weld parameters through the output layer. Based on the prediction error between the predicted weld parameters and the target weld parameters in the target training sample data, the weights and thresholds of the initial welding prediction model are updated.
[0010] Optionally, the training data set is obtained, including: Obtain the position data of the first material to be welded and the position data of the second material; Obtain the actual weld parameters corresponding to the first material and the second material; A training dataset is obtained based on the position data of the first material to be welded, the position data of the second material, and the actual weld parameters; wherein, the actual weld parameters are used as the target weld parameters of the training dataset.
[0011] Secondly, this application provides a deviation correction detection system, comprising: The acquisition module is used to acquire the position data of the first material to be welded and the position data of the second material, as well as the target weld parameters corresponding to the first material and the second material; The prediction module is used to predict the predicted weld parameters corresponding to the first material and the second material based on the position data of the first material and the position data of the second material using a welding prediction model. The correction module is used to determine a first moving distance corresponding to a first material and / or a second material based on predicted weld parameters and target weld parameters; control the correction mechanism to perform correction actions based on the first moving distance; and when the actual weld parameters corresponding to the first material and the second material are received, determine a second moving distance corresponding to the first material and / or the second material based on the actual weld parameters and the target weld parameters; and control the correction mechanism to perform correction actions based on the second moving distance.
[0012] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described correction detection method.
[0013] Fourthly, this application provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the aforementioned correction detection method.
[0014] This application provides a method, system, device, and medium for corrective welding detection. The method involves acquiring position data of a first material and a second material to be welded, as well as target weld parameters. Based on the position data of the first and second materials, a welding prediction model is used to predict the predicted weld parameters corresponding to the first and second materials. Based on the predicted weld parameters and the target weld parameters, a first moving distance corresponding to the first and / or second materials is determined. Based on the first moving distance, a corrective mechanism is controlled to perform a corrective action. Upon receiving the actual weld parameters corresponding to the first and second materials, a second moving distance corresponding to the first and / or second materials is determined based on the actual weld parameters and the predicted weld parameters. Based on the second moving distance, the corrective mechanism is controlled to perform a corrective action, thereby improving both production efficiency and welding accuracy.
[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 A schematic diagram of the structure of a correction detection device provided in an embodiment of the present invention is shown; Figure 2 A schematic flowchart of a deviation correction detection method provided by an embodiment of the present invention is shown; Figure 3 A flowchart illustrating the training method of the welding prediction model provided in an embodiment of the present invention is shown. Figure 4 A schematic diagram of the structure of a deviation correction detection system provided in an embodiment of the present invention is shown; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present invention is shown. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0019] This application provides a deviation correction detection device, see below. Figure 1 As shown, the deviation correction and detection device provided in this application includes an unwinding mechanism, a deviation correction mechanism, a first detection mechanism, a welding mechanism, a second detection mechanism, and a rewinding mechanism connected in sequence; wherein, the unwinding mechanism is used to store the first material and the second material to be welded, the deviation correction mechanism is used to adjust the position of the first material and / or the second material based on the detection results of the first detection mechanism and the second detection mechanism, the first detection mechanism is used to use the position data of the first material and the second material, the welding mechanism is used to weld the first material and the second material, the second detection mechanism is used to collect the weld position of the first material and / or the second material, and the rewinding mechanism is used to rewind the first material and the second material after welding; The deviation correction and detection device also includes a control mechanism, which is signal-connected to the unwinding mechanism, the deviation correction mechanism, the first detection mechanism, the welding mechanism, the second detection mechanism, and the rewinding mechanism. The control mechanism is used to predict the predicted weld parameters corresponding to the first and second materials based on the position data of the first and second materials collected by the first detection mechanism using a welding prediction model. Based on the predicted weld parameters and the target weld parameters, it determines the first moving distance corresponding to the first and / or second materials. Based on the first moving distance, it controls the deviation correction mechanism to perform deviation correction actions. Based on the actual weld parameters and the target weld parameters collected by the second detection mechanism, it determines the second moving distance corresponding to the first and / or second materials. Based on the second moving distance, it controls the deviation correction mechanism to perform deviation correction actions.
[0020] In this embodiment of the application, the specific working process of the correction detection device is as follows: The unwinding mechanism stores the first and second materials to be welded, such as rolls or sheets of metal foil or film sheets, and simultaneously conveys the first and second materials to the first inspection mechanism through the correction mechanism according to the preset speed and tension. The first testing unit collects the relative position data of the first material and the second material in real time in the testing area, such as the alignment deviation between the edges of the first material and the second material, the overlap width deviation, the offset of the central axis, and other key parameters, and transmits the position data to the control unit in real time. Based on position data, the control mechanism uses a welding prediction model to predict the predicted weld parameters corresponding to the first material and the second material. Based on the predicted weld parameters and the target weld parameters, it determines the first moving distance corresponding to the first material and / or the second material. Based on the first moving distance, it controls the correction mechanism to perform correction actions. The correction mechanism drives the actuators such as lead screw modules, cylinders, and servo motors to adjust the position of any one or both of the first and second materials, such as left and right translation and angle fine adjustment, until the relative position of the first and second materials meets the welding requirements. The welding mechanism welds the joint or lap joint of the first and second materials according to preset processes such as laser welding, ultrasonic welding and hot-press welding to form a stable connection weld. The second inspection unit collects the actual position data of the weld, such as the distance deviation between the weld and the material edge, the straightness of the weld, and the offset of the weld in the width direction of the material, and sends the weld position data to the control unit. The control mechanism determines the second moving distance corresponding to the first material and / or the second material based on the actual weld parameters and the target weld parameters; based on the second moving distance, it controls the correction mechanism to perform the correction action; The correction mechanism drives the actuator again to adjust the position of one or both of the first and second materials, such as left and right translation and angular fine adjustment, until the relative position of the first and second materials meets the welding requirements.
[0021] The deviation correction and detection device provided in this application improves production efficiency and welding accuracy through a closed-loop mode of pre-detection and deviation correction and post-weld verification.
[0022] This application provides a method for error correction detection, see below. Figure 2 As shown, the general flow of the deviation correction detection method provided in this application embodiment is as follows: Step 110: Obtain the position data of the first material to be welded and the position data of the second material, as well as the target weld parameters corresponding to the first material and the second material.
[0023] In this embodiment of the application, the position data of the first material to be welded, the position data of the second material, and the target weld parameters can be obtained in the following ways: The first sensor of the first detection mechanism set on the production line collects parameters such as the edge, central axis, offset, and offset angle of the first material as the position data of the first material; The second sensor of the first detection mechanism set on the production line collects parameters such as the edge, central axis, offset, and offset angle of the second material as position data of the second material; Obtain the target weld parameters determined by the operator according to the welding process requirements. The target weld parameters include, but are not limited to, the distance threshold between the weld and the edge of the first material, the corresponding position of the weld and the feature point of the second material, the straightness and width of the weld, etc.
[0024] Step 120: Based on the position data of the first material and the position data of the second material, use a welding prediction model to predict the predicted weld parameters corresponding to the first material and the second material; based on the predicted weld parameters and the target weld parameters, determine the first moving distance corresponding to the first material and / or the second material; based on the first moving distance, control the correction mechanism to perform correction actions.
[0025] In this embodiment of the application, determining the first moving distance corresponding to the first material and / or the second material includes: determining a first vector corresponding to the predicted weld parameters using a vector operation algorithm based on the predicted weld parameters; determining a second vector corresponding to the target weld parameters using a vector operation algorithm based on the target weld parameters; determining the first moving distance corresponding to the first material and / or the second material based on the first vector and the second vector, wherein the moving direction corresponding to the first moving distance is determined based on the first vector and the second vector; and determining the adjustment of the first material and / or the second material corresponding to the first moving distance based on the moving direction.
[0026] In specific implementation, the position data of the first material and the position of the second material are input into the pre-trained welding prediction model. The welding prediction model determines the predicted weld parameters corresponding to the first material and the second material based on the position data of the first material and the position of the second material. The welding prediction model can be a deep learning neural network model or a traditional machine learning regression model. Extract the core feature points for predicting weld parameters, such as the coordinates of the weld start point, midpoint, and end point. Use the weld midpoint coordinates as an example. With this as the endpoint, the first vector (i.e., the predicted weld vector) is generated based on the vector operation algorithm. The direction of the first vector represents the predicted weld orientation, and the modulus... This represents the predicted distance from the midpoint of the weld to the reference point. Using the coordinates of the feature points of the target weld With the endpoint being [the target weld vector], a second vector (i.e., the target weld vector) is generated based on a vector operation algorithm. The direction and magnitude of the second vector are the standard values for the weld position; Determine the deviation vector based on the first and second vectors. =( The direction of the deviation vector is the first moving direction of the first material and / or the second material, and the modulus is the first moving distance.
[0027] Furthermore, based on the magnitude of the deviation vector, the stiffness coefficient of the material, and the response characteristics of the correction mechanism, the optimized first moving distance is obtained through inverse operation correction.
[0028] In this embodiment, the correction mechanism is controlled to perform correction actions based on the first moving distance, which can be achieved in the following ways: determining a first offset distance based on the position data of the first material and the first moving distance; determining a second offset distance based on the position data of the second material and the first moving distance; and controlling the correction mechanism to adjust the position of the first material and / or the second material based on the first offset distance and the second offset distance. Specifically, when the first offset distance is greater than the second offset distance, the correction mechanism is controlled to adjust the position of the first material to make the first offset distance and the second offset distance equal; when the first offset distance is less than the second offset distance, the correction mechanism is controlled to adjust the position of the second material to make the first offset distance and the second offset distance equal; and when the first offset distance and the second offset distance are equal, the correction mechanism is controlled to adjust the position of the first material and the second material to make the first offset distance and the second offset distance zero.
[0029] In practice, based on the position data of the first material and the first moving distance, the first offset distance is determined. The first offset distance is the difference between the current position of the first material and the correction target position. ,in, This is the location data of the first material. The ideal position of the first material, derived from the first moving distance, can be determined using the position data of the first material and the pre-adjustment distance. Based on the position data of the second material and the first moving distance, a second offset distance is determined. The second offset distance is the difference between the current position of the second material and the target position for correction. ,in, For the location data of the second material, The ideal position of the second material, derived from the second moving distance, can be determined using the position data of the second material and the pre-adjustment distance. When the first offset distance is greater than the second offset distance, the correction mechanism is controlled to adjust the position of the first material so that the first offset distance and the second offset distance are equal; when the first offset distance is less than the second offset distance, the correction mechanism is controlled to adjust the position of the second material so that the first offset distance and the second offset distance are equal; when the first offset distance and the second offset distance are equal, the correction mechanism is controlled to adjust the position of the first material and the second material so that the first offset distance and the second offset distance are zero. By prioritizing the adjustment of materials with large deviations and then simultaneously adjusting materials with similar deviations, the problem of over-adjustment caused by differences in material rigidity in traditional synchronous or individual adjustments is solved, thus achieving precise alignment.
[0030] Step 130: Upon receiving the actual weld parameters corresponding to the first material and the second material, determine the second moving distance corresponding to the first material and / or the second material based on the actual weld parameters and the target weld parameters; based on the second moving distance, control the correction mechanism to perform the correction action.
[0031] In this embodiment of the application, when determining the second moving distance corresponding to the first material and / or the second material based on the actual weld parameters and the target weld parameters, the weld deviation value is calculated by comparing the actual weld parameters and the target weld parameters item by item. The weld deviation value includes, but is not limited to, lateral deviation, longitudinal deviation and angular deviation. Based on the second moving distance, the correction mechanism is controlled to perform a correction action to move the positions of the first material and the second material to the target positions. The determination of the second moving distance and the control of the correction mechanism to perform the correction action are consistent with the determination of the first moving distance and the control of the correction mechanism to perform the correction action described above.
[0032] The deviation correction detection method provided in this application achieves high precision and adaptive position calibration for dual-material welding through dual deviation correction operations of pre-correction before welding and closed-loop optimization after welding.
[0033] This application provides a training method for a welding prediction model, see embodiments below. Figure 3 As shown, the general flow of the training method for the welding prediction model provided in this application embodiment is as follows: Step 310: Obtain the training data set; wherein, the training data set includes multiple training sample data; each training sample data includes the position data of the first material to be welded and the position data of the second material, as well as the target weld parameters; In this embodiment of the application, the training data set can be obtained in the following ways: Obtain the position data of the first material to be welded and the position data of the second material; Obtain the actual weld parameters corresponding to the first material and the second material; A training dataset is obtained based on the position data of the first material to be welded, the position data of the second material, and the actual weld parameters; wherein, the actual weld parameters are used as the target weld parameters of the training dataset.
[0034] Step 320: Select target training sample data from the training dataset.
[0035] Step 330: Input the position data of the first material and the position data of the second material in the target training sample data into the initial welding prediction model, so that the initial welding prediction model receives the position data of the first material and the position data of the second material through the input layer, processes the position data of the second material and the position data of the second material through the hidden layer to obtain the predicted weld parameters, and then outputs the predicted weld parameters through the output layer.
[0036] Step 340: Based on the prediction error between the predicted weld parameters and the target weld parameters in the target training sample data, update the weights and thresholds of the initial welding prediction model.
[0037] Step 350: Determine whether the iterative training termination condition is met; if yes, proceed to step 360; if no, return to step 320; wherein, the iterative training termination condition is that the number of iterations is not less than the number threshold, or the prediction error is not higher than the error threshold.
[0038] Step 360: Based on the weights and thresholds of the initial welding prediction model updated during the last iteration of training, obtain the welding prediction model.
[0039] This application provides a deviation correction detection system, see below. Figure 4 As shown, the deviation correction detection system provided in this application embodiment includes: The acquisition module 410 is used to acquire the position data of the first material to be welded and the position data of the second material, as well as the target weld parameters corresponding to the first material and the second material; the prediction module 420 is used to predict the predicted weld parameters corresponding to the first material and the second material based on the position data of the first material and the position data of the second material using a welding prediction model. The correction module 430 is used to determine a first moving distance corresponding to a first material and / or a second material based on predicted weld parameters and target weld parameters; control the correction mechanism to perform correction actions based on the first moving distance; and when receiving actual weld parameters corresponding to the first material and the second material, determine a second moving distance corresponding to the first material and / or the second material based on the actual weld parameters and the target weld parameters; and control the correction mechanism to perform correction actions based on the second moving distance.
[0040] It should be noted that the principle of the correction detection system provided in this application embodiment to solve the technical problem is similar to that of the correction detection method provided in this application embodiment. Therefore, the implementation of the correction detection system provided in this application embodiment can refer to the implementation of the correction detection method provided in this application embodiment, and the repeated parts will not be described again.
[0041] After introducing the correction detection method and apparatus provided in the embodiments of this application, the electronic equipment provided in the embodiments of this application will be briefly introduced next.
[0042] See Figure 5 As shown, the electronic device 500 provided in this application embodiment includes at least a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program, it implements the deviation correction detection method provided in this application embodiment.
[0043] The electronic device 500 provided in this application embodiment may further include a bus 503 connecting different components (including processor 501 and memory 502). The bus 503 represents one or more types of bus structures, including memory bus, peripheral bus, local area bus, etc.
[0044] Memory 502 may include a readable storage medium in the form of volatile memory, such as random access memory (RAM) 5021 and / or cache memory 5022, and may further include read-only memory (ROM) 5023. Memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, including but not limited to an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.
[0045] Processor 501 can be a single processing element or a collective term for multiple processing elements. For example, processor 501 can be a central processing unit (CPU) or one or more integrated circuits configured to implement the deviation correction detection method provided in the embodiments of this application. Specifically, processor 501 can be a general-purpose processor, including but not limited to CPUs, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
[0046] Electronic device 500 can communicate with one or more external devices 504 (e.g., keyboard, remote control, etc.), and also with one or more devices that enable a user to interact with electronic device 500 (e.g., mobile phone, computer, etc.), and / or with devices that enable electronic device 500 to communicate with one or more other electronic devices 500 (e.g., router, modem, etc.). This communication can be performed through input / output (I / O) interface 505. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 506. Figure 5 As shown, network adapter 506 communicates with other modules of electronic device 500 via bus 503. It should be understood that, although... Figure 5 As not shown, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) subsystems, tape drives, and data backup storage subsystems.
[0047] It should be noted that, Figure 5 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.
[0048] The computer-readable storage medium provided in the embodiments of this application is described below. The computer-readable storage medium provided in the embodiments of this application stores computer instructions, which, when executed by a processor, implement the deviation correction detection method provided in the embodiments of this application. Specifically, the computer instructions can be built into or installed in the processor, so that the processor can implement the deviation correction detection method provided in the embodiments of this application by executing the built-in or installed computer instructions.
[0049] In addition, the deviation correction detection method provided in this application embodiment can also be implemented as a computer program product, which includes program code. The program code implements the deviation correction detection method provided in this application embodiment when it runs on a processor.
[0050] The computer program product provided in this application embodiment may employ one or more computer-readable storage media, which may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. Specifically, more specific examples (a non-exhaustive list) of computer-readable storage media include electrical connections with one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0051] The computer program product provided in this application embodiment can be a CD-ROM and include program code, and can also run on electronic devices such as computers. However, the computer program product provided in this application embodiment is not limited thereto. In this application embodiment, the computer-readable storage medium can be any tangible medium that contains or stores program code, which can be used by or in conjunction with an instruction execution system, device, or apparatus.
[0052] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.
[0053] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.
[0054] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.
[0055] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.
Claims
1. A method for detecting deviation correction, characterized in that, include: Obtain the position data of the first material to be welded and the position data of the second material, as well as the target weld parameters corresponding to the first material and the second material; Based on the position data of the first material and the position data of the second material, a welding prediction model is used to predict the predicted weld parameters corresponding to the first material and the second material; based on the predicted weld parameters and the target weld parameters, a first moving distance corresponding to the first material and / or the second material is determined; based on the first moving distance, the correction mechanism is controlled to perform correction actions. Upon receiving the actual weld parameters corresponding to the first material and the second material, a second moving distance corresponding to the first material and / or the second material is determined based on the actual weld parameters and the target weld parameters; based on the second moving distance, the correction mechanism is controlled to perform a correction action.
2. The correction detection method according to claim 1, characterized in that, Based on the predicted weld parameters and the target weld parameters, determining the first moving distance corresponding to the first material and / or the second material includes: Based on the predicted weld parameters, a vector operation algorithm is used to determine the first vector corresponding to the predicted weld parameters; Based on the target weld parameters, a vector operation algorithm is used to determine the second vector corresponding to the target weld parameters; Based on the first vector and the second vector, determine the first moving distance corresponding to the first material and / or the second material.
3. The correction detection method according to claim 2, characterized in that, Based on the first vector and the second vector, determining the first movement distance corresponding to the first material and / or the second material includes: Based on the first vector and the second vector, determine the direction of movement corresponding to the first movement distance; Based on the direction of movement, the first moving distance is determined to correspond to the adjustment of the first material and / or the second material.
4. The correction detection method according to claim 1, characterized in that, Based on the first moving distance, control the correction mechanism to perform a correction action, including: Based on the position data of the first material and the first moving distance, the first offset distance is determined; Based on the location data of the second material and the first moving distance, the second offset distance is determined; Based on the first offset distance and the second offset distance, the correction mechanism is controlled to adjust the position of the first material and / or the second material.
5. The correction detection method according to claim 4, characterized in that, Based on the first offset distance and the second offset distance, controlling the correction mechanism to adjust the position of the first material and / or the second material includes: When the first offset distance is greater than the second offset distance, the correction mechanism is controlled to adjust the position of the first material so that the first offset distance and the second offset distance are equal. When the first offset distance is less than the second offset distance, the correction mechanism is controlled to adjust the position of the second material so that the first offset distance and the second offset distance are equal. When the first offset distance and the second offset distance are equal, the correction mechanism is controlled to adjust the positions of the first material and the second material so that the first offset distance and the second offset distance are zero.
6. The correction detection method according to claim 1, characterized in that, Also includes: Obtain a training data set; wherein the training data set includes multiple training sample data; each training sample data includes the position data of the first material to be welded and the position data of the second material, as well as the target weld parameters; Based on the training dataset, an iterative training operation is performed on the initial welding prediction model until the iterative training termination condition is met. Then, based on the weights and thresholds of the initial welding prediction model updated during the last execution of the iterative training operation, the welding prediction model is obtained; wherein, the iterative training operation includes: Select target training sample data from the training dataset; The position data of the first material and the position data of the second material in the target training sample data are input into the initial welding prediction model, so that the initial welding prediction model receives the position data of the first material and the position data of the second material through the input layer, processes the position data of the second material and the position data of the second material through the hidden layer to obtain the predicted weld parameters, and then outputs the predicted weld parameters through the output layer. Based on the prediction error between the predicted weld parameters and the target weld parameters in the target training sample data, the weights and thresholds of the initial welding prediction model are updated.
7. The correction detection method as described in claim 6, characterized in that, Obtain the training dataset, including: Obtain the position data of the first material to be welded and the position data of the second material; Obtain the actual weld parameters corresponding to the first material and the second material; The training data set is obtained based on the position data of the first material to be welded and the position data of the second material, as well as the actual weld parameters; wherein the actual weld parameters serve as the target weld parameters of the training data set.
8. A deviation correction detection system, characterized in that, include: The acquisition module is used to acquire the position data of the first material to be welded and the position data of the second material, as well as the target weld parameters corresponding to the first material and the second material; The prediction module is used to predict the predicted weld parameters corresponding to the first material and the second material based on the position data of the first material and the position data of the second material using a welding prediction model; The correction module is configured to determine a first moving distance corresponding to the first material and / or the second material based on the predicted weld parameters and the target weld parameters; control the correction mechanism to perform correction actions based on the first moving distance; and when the actual weld parameters corresponding to the first material and the second material are received, determine a second moving distance corresponding to the first material and / or the second material based on the actual weld parameters and the target weld parameters; and control the correction mechanism to perform correction actions based on the second moving distance.
9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the deviation correction detection method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the deviation correction detection method as described in any one of claims 1 to 7.